Reconfigurable tasks in belief-space planning
Reconfigurable tasks in belief-space planning
复制标题
信念空间规划中的可重构任务
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
R. Grupen
中科院分区：
文献类型：
--
作者：
Dirk Ruiken;Tiffany Q. Liu;Takeshi Takahashi;R. Grupen
我们提出了一个任务表示使用的信念空间规划框架。该表示基于专用对象模型，该专用对象模型使得能够估计机器人相对于对象的抽象状态。每个操作任务使用由已知对象模型的集合定义的这些状态上的分区来表示。这样的任务的解决方案中构建的信念空间规划器使用视觉和/或手动与对象的相互作用，凝聚在任务分区的目标子集的信念。这种划分将状态上的信念整合到任务信念中，而不改变原始的信念表示。因此，可以处理继承整个观测历史上的完整状态估计的任务序列。演示的技术在模拟和一个真实的机器人。结果表明，使用此任务表示和信念空间规划器，机器人能够识别对象，找到目标对象，并操纵一组对象，以获得所需的状态。
We propose a task representation for use in a belief-space planning framework. The representation is based on specialized object models that enable estimation of an abstract state of a robot with respect to an object. Each manipulation task is represented using a partition over these states defined by the set of known object models. Solutions to such tasks are constructed in a belief-space planner using visual and/or manual interactions with objects that condense belief in a target subset of the task partition. This partition integrates belief over states into a task belief without altering the original belief representation. As a result, sequences of tasks can be addressed that inherit the complete estimate of state over the entire history of observations. Demonstrations of the technique are presented in simulation and on a real robot. Results show that using this task representation and the belief-space planner, the robot is able to recognize objects, find target objects, and manipulate a set of objects to obtain a desired state.